The Novelty Bottleneck: A Framework for Understanding Human Effort Scaling in AI-Assisted Work

We propose a stylized model of human-AI collaboration that isolates a mechanism we call the novelty bottleneck: the fraction of a task requiring human judgment creates an irreducible serial component analogous to Amdahl's Law in parallel computing. The model assumes that tasks decompose into atomic decisions, a fraction $ν$ of which are "novel" (not covered by the agent's prior), and that specification, verification, and error correction each scale with task size. From these assumptions, we deri

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ethics.ai (28 March 2026), “The Novelty Bottleneck: A Framework for Understanding Human Effort Scaling in AI-Assisted Work,” evidence record 6631, https://ethics.ai/record/6631 (originally published by arXiv).

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